Transformative Applications of Generative AI in Socially Responsible Financial Models: A Machine Learning Perspective

Abstract

In a financial decision-making process, an investor or stakeholder seeks to balance several of his or her financial objectives, which may include maximizing investment returns, meeting long-term growth and expansion goals of a business enterprise, and synergizing his or her interests with modern business management strategies that promote sustainable development. When seeking to actualize the latter goal, stakeholders are usually guided by a socially responsible framework to ensure sustainable financial operations. However, sustainability-linked decision-making processes may require the application of several innovative tools and methodologies. With the growing prominence of generative AI, it is expected that some applications could be leveraged to develop societies, economies, firms, families, and investors as desired in socially responsible financial models. To provide a direction that ensures the maximum use of generative AI, this essay not only discusses such generative AI applications but also provides some machine learning-inspired solutions.

Fostering socially responsible financial solutions could become extensive and effective if generative AI is trained to harness the combination of risky assets that could ensure financially sustainable firms while improving the population and economy. Alternative asset allocation strategies in this context, although available in the existing finance literature, do not proportionally reflect a challenging interplay of the debilitating population growth movement with investment opportunity trends. Hence, to feed finance with a more responsible, accurate, and humane side, machine learning could be utilized to mine the first-generation data and see how blends offer a guarantee of a good life. This is a completely new way to fix our gaze on finance in literature. A mere normative approach is therefore abandoned to escape from the vagueness of existing social portfolio creation mechanisms. This innovative role of machine learning is our novel contribution to research.